class_docstr: Base class for text encoder
class_docstr_tok: ["Base","class","for","text","encoder"]
docstr: Create dummy input and labels for CI/CD purposes. Child classes must override it
  if functionality is different.
docstr_tok: ["Create","dummy","input","and","labels","for","CI\/CD","purposes",".","Child","classes","must","override","it","if","functionality","is","different","."]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """Create dummy input and labels for CI/CD purposes. Child classes must override it
      if functionality is different.
      """
      seq_length = 77
      vocab_size = 10
      text_tensor = torch.randint(
          low=0, high=vocab_size, size=(batch_size, seq_length)
      ).long()
      return {"text": text_tensor}
  
file_path: ml-cvnets/cvnets/text_encoders/base_text_encoder.py
imports: import argparse
  from typing import Any, Dict, Optional
  import torch
  from torch import Tensor, nn
  from cvnets import parameter_list
  from cvnets.layers import norm_layers_tuple
  from cvnets.misc.init_utils import initialize_weights
  from utils import logger
  from utils.ddp_utils import is_master
label_desc: Use image node_id 7 for calling the BaseTextEncoder obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict